Python Performance Boost: I/O vs CPU-bound Code

Day 3 of my Python journey, and I’ve officially hit my first "ego check." Yesterday, I thought I had a simple Even/Odd script figured out. I was wrong. It turns out the computer doesn't care what I meant; it only cares what I wrote. ❌ Moving into Day 3, I’ve been studying the "Laws of the Python Universe" to stop guessing and start structuring: 🔹 The Hierarchy of Operations: I learned that 1 + 2**3 / 4 * 5 isn’t just a string of numbers—it’s a sequence. Python doesn't just read left-to-right; it respects a hierarchy (PEMDAS). If you ignore the order of operations, your data is junk before you even hit 'Enter.' 🔹 The "Wait" State: I built a simple script to convert European floor numbers to US floors. It’s a basic + 1 calculation, but it taught me about "Blocking Calls"—how the program literally pauses its entire existence to wait for the user to provide data. The biggest takeaway? Coding isn't about memorizing syntax; it's about debugging my own thought process. I’m learning that "clean code" starts with a "clear mind." Day 4 is up next. Let’s see if I can outsmart the compiler tomorrow. 🐍 #Python #LearningToCode #BuildInPublic #SoftwareLogic #TechJourney #DataScience

I built a very rudimentary AI agent with python last weekend in VS Code, and here’s what I learnt (no hype). Firstly: The only tools I gave the agent were “read_file” and “write_file”… when I say rudimentary, I really do mean it 😄 Takeaways: 1. An agent is fundamentally an LLM with the ability to discern tool use (a tool is python code or an api call to another platform), and decide when the task is complete (looping). 2. If you’re not a developer, building an agent outside of an orchestration platform isn’t a cake walk. I have a foundational understanding of python, but I wouldn’t have been able to write the code myself. I had to rely heavily on iteration and explanation with an LLM whilst building it out. 3. The development architecture of an agent is worth understanding. Files for tools, for the user prompt, for the agent itself, nestled within a project structure. And, lastly a perception: 4. After having done it, I’m now slightly more convinced that the number of people saying they’ve built an agent vs the actual number of agents built is probably quite exaggerated. But I’d be keen to learn how you’ve built an agent, what you did, and what the agent’s function was?

Eight years ago, I was building highly concurrent systems running AI workloads in Elixir and Python. At that time, scaling AI services efficiently wasn’t mainstream yet. We had to design concurrency-first architectures, optimize performance, and solve problems that are now common in modern AI systems. I open-sourced part of that work back then: 🐍 Piton – a Python library designed for high-concurrency, AI-driven systems. Sharing it again today because the challenges it addresses are more relevant than ever. https://lnkd.in/dkk9W8M #Elixir #Python #AI #Concurrency #DistributedSystems #OpenSource

Python’s "Rustification" is no longer a trend, It’s the standard ! 🦀🐍 The Python ecosystem is undergoing a massive transformation. We are moving away from the "slow but flexible" reputation and toward a future that is blisteringly fast and memory-efficient, thanks to Rust. If you want to build a modern stack, these three tools from Astral and the Polars team are the new "Big Three": ⚡ Ruff: The Linter/Formatter - The Vibe: Why wait seconds for Black or Flake8? - The Power: Replaces almost your entire linting stack with a single Rust binary. It’s so fast you can run it on every file-save without a hint of lag. 📦 uv: The Package Manager - The Vibe: Package management that finally feels modern. - The Power: A drop-in replacement for pip and poetry. It resolves dependencies and creates virtual environments in milliseconds, not minutes. 🚀 Polars: The Data Engine - The Vibe: Moving past the "Pandas memory wall." - The Power: A multi-threaded, vectorized query engine written in Rust. It doesn’t just process data faster; it processes larger datasets on your local machine by being incredibly smart about memory and CPU cores. The Common Thread? They all leverage Rust to do the heavy lifting while keeping the Pythonic API we love. It’s the best of both worlds: developer productivity meets industrial-grade performance. #Python #Polars #Rust #DataEngineering #DataScience #Astral #TechTrends 

Let’s keep it simple (and a little fun today 😄) Which of the following is a valid list comprehension in Python? A) [x for x in range(5) if x % 2 == 0] B) for x in range(5): if x % 2 == 0 C) x = [range(5) if x % 2 == 0] D) list(x for x in range(5) if x % 2 == 0) 🧠 The correct answer is: 👉 A [x for x in range(5) if x % 2 == 0] This is the classic Pythonic structure: [expression for item in iterable if condition] ✨ Option D technically works and returns a list, but A is the clean, direct list comprehension syntax. Small concept. Big readability difference. Clean code isn’t about writing more… It’s about writing smarter 🔥 #Python #AI #LearningInPublic #30DayChallenge #DataAnalytics #CleanCode

Readability scales better than cleverness. Early in my career, I enjoyed writing compact and “smart” Python code — list comprehensions everywhere, chained expressions, one-liners that looked elegant. But as projects grew and teams got larger, I realized something important: Code is read far more often than it is written. A simple loop is often better than a clever comprehension. A clear variable name beats a short one. Explicit logic beats implicit magic. Python’s philosophy says it clearly: “Readability counts.” And in production systems, readability is not just style — it’s operational safety. When a bug appears at 2 AM, the best code is the one that any engineer can understand quickly. Great Python developers are not the ones who write the most clever code. They are the ones who write code that ages well. Hashtags #Python #SoftwareEngineering #CleanCode #BackendDevelopment #ProgrammingLessons

Most people think AI agents are complex, mysterious systems. They're not. An agent is a thermostat. It reads a sensor (your prompt). It compares to a target (the task). It triggers an action (calls a tool). Then it waits and repeats. That's it. That's the architecture behind Claude Code, Cursor, and Copilot. The difference between understanding this and not? When it breaks, you know exactly which line caused it. We wrote a book that teaches you to build one from scratch in 750 lines of Python. No frameworks. No magic. Follow this page for more posts like this. https://lnkd.in/gWdFWM4g #AIAgents #Python #SoftwareEngineering #LLM

This metaphor changed how I explain AI agents to non-technical people. Once you say "thermostat," the mystique evaporates — and what's left is just engineering. That's why I wrote the book.

Ever explained Duck Typing in Python to someone and watched their face go from 😃 → 🤯 in 3 seconds? Here’s how I tried explaining it to a friend: Friend: “How does Python know if something is a duck?” Me: Python doesn’t care if it’s a duck 🦆, a robot 🤖, or a developer pretending to work on Friday afternoon 😅 If it walks like a duck and quacks like a duck, Python just says: "Cool… must be a duck." Example 👇 class Duck: def quack(self): print("Quack!") class Person: def quack(self): print("I can imitate a duck!") def make_it_quack(obj): obj.quack() make_it_quack(Duck()) make_it_quack(Person()) Python: "Both quack? Perfect. I’m not asking for ID." Meanwhile in some other languages: "Excuse me sir, please submit 4 forms, 2 interfaces, and a type certificate before quacking." 🧾 That’s the beauty of Python — behavior matters more than type. So remember: In Python, nobody asks what you are. They only check what you can do. And honestly… that’s a life lesson too. 😄 #Python #DuckTyping #ProgrammingHumor #LearnToCode #PythonDeveloper #CodingLife #SoftwareEngineering #TechHumor #DeveloperLife